1,330 research outputs found
High Impedance Detector Arrays for Magnetic Resonance
Resonant inductive coupling is commonly seen as an undesired fundamental
phenomenon emergent in densely packed resonant structures, such as nuclear
magnetic resonance phased array detectors. The need to mitigate coupling
imposes rigid constraints on the detector design, impeding performance and
limiting the scope of magnetic resonance experiments. Here we introduce a high
impedance detector design, which can cloak itself from electrodynamic
interactions with neighboring elements. We verify experimentally that the high
impedance detectors do not suffer from signal-to-noise degradation mechanisms
observed with traditional low impedance elements. Using this new-found
robustness, we demonstrate an adaptive wearable detector array for magnetic
resonance imaging of the hand. The unique properties of the detector glove
reveal new pathways to study the biomechanics of soft tissues, and exemplify
the enabling potential of high-impedance detectors for a wide range of
demanding applications that are not well suited to traditional coil designs.Comment: 16 pages, 12 figures, videos available upon reques
Reconstruction strategy for echo planar spectroscopy and its application to partially undersampled imaging.
The most commonly encountered form of echo planar spectroscopy involves oscillating gradients in one spatial dimension during readout. Data are consequently not sampled on a Cartesian grid. A fast gridding algorithm applicable to this particular situation is presented. The method is optimal, i.e., it performs as well as the full discrete Fourier transform for band limited signals while allowing for use of the fast Fourier transform. The method is demonstrated for reconstruction of data that are partially undersampled in the time domain. The advantages of undersampling are lower hardware requirements or fewer interleaves per acquisition. The method is of particular interest when large bandwidths are needed (e.g., for high field scanning) and for scanners with limited gradient performance. The unavoidable artifacts resulting from undersampling are demonstrated to be acceptable for spectroscopy with long echo times
Optimized Quantification of Spin Relaxation Times in the Hybrid State
Purpose: The analysis of optimized spin ensemble trajectories for relaxometry
in the hybrid state.
Methods: First, we constructed visual representations to elucidate the
differential equation that governs spin dynamics in hybrid state. Subsequently,
numerical optimizations were performed to find spin ensemble trajectories that
minimize the Cram\'er-Rao bound for -encoding, -encoding, and their
weighted sum, respectively, followed by a comparison of the Cram\'er-Rao bounds
obtained with our optimized spin-trajectories, as well as Look-Locker and
multi-spin-echo methods. Finally, we experimentally tested our optimized spin
trajectories with in vivo scans of the human brain.
Results: After a nonrecurring inversion segment on the southern hemisphere of
the Bloch sphere, all optimized spin trajectories pursue repetitive loops on
the northern half of the sphere in which the beginning of the first and the end
of the last loop deviate from the others. The numerical results obtained in
this work align well with intuitive insights gleaned directly from the
governing equation. Our results suggest that hybrid-state sequences outperform
traditional methods. Moreover, hybrid-state sequences that balance - and
-encoding still result in near optimal signal-to-noise efficiency. Thus,
the second parameter can be encoded at virtually no extra cost.
Conclusion: We provide insights regarding the optimal encoding processes of
spin relaxation times in order to guide the design of robust and efficient
pulse sequences. We find that joint acquisitions of and in the
hybrid state are substantially more efficient than sequential encoding
techniques.Comment: 10 pages, 5 figure
Структура и закономерности науки
Cardiovascular MR imaging (CVMR) has become a valuable diagnostic imaging modality for the non-invasive detection cardiovascular diseases. In this review, first key concepts and practical considerations of parallel CVMR are outlined. Next, highly accelerated CVMR applications are reviewed, ranging from cardiac anatomical and functional assessment to myocardial perfusion and viability to MR angiography of the coronary arteries and the large vessels. Finally, current trends, including the broad move towards high field imaging, and future directions in highly parallel CVMR are considered..
Hybrid-State Free Precession in Nuclear Magnetic Resonance
The dynamics of large spin-1/2 ensembles in the presence of a varying
magnetic field are commonly described by the Bloch equation. Most magnetic
field variations result in unintuitive spin dynamics, which are sensitive to
small deviations in the driving field. Although simplistic field variations can
produce robust dynamics, the captured information content is impoverished.
Here, we identify adiabaticity conditions that span a rich experiment design
space with tractable dynamics. These adiabaticity conditions trap the spin
dynamics in a one-dimensional subspace. Namely, the dynamics is captured by the
absolute value of the magnetization, which is in a transient state, while its
direction adiabatically follows the steady state. We define the hybrid state as
the co-existence of these two states and identify the polar angle as the
effective driving force of the spin dynamics. As an example, we optimize this
drive for robust and efficient quantification of spin relaxation times and
utilize it for magnetic resonance imaging of the human brain
On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis
Magnetic Resonance Imaging (MRI) is considered the gold standard of medical
imaging because of the excellent soft-tissue contrast exhibited in the images
reconstructed by the MRI pipeline, which in-turn enables the human radiologist
to discern many pathologies easily. More recently, Deep Learning (DL) models
have also achieved state-of-the-art performance in diagnosing multiple diseases
using these reconstructed images as input. However, the image reconstruction
process within the MRI pipeline, which requires the use of complex hardware and
adjustment of a large number of scanner parameters, is highly susceptible to
noise of various forms, resulting in arbitrary artifacts within the images.
Furthermore, the noise distribution is not stationary and varies within a
machine, across machines, and patients, leading to varying artifacts within the
images. Unfortunately, DL models are quite sensitive to these varying artifacts
as it leads to changes in the input data distribution between the training and
testing phases. The lack of robustness of these models against varying
artifacts impedes their use in medical applications where safety is critical.
In this work, we focus on improving the generalization performance of these
models in the presence of multiple varying artifacts that manifest due to the
complexity of the MR data acquisition. In our experiments, we observe that
Batch Normalization, a widely used technique during the training of DL models
for medical image analysis, is a significant cause of performance degradation
in these changing environments. As a solution, we propose to use other
normalization techniques, such as Group Normalization and Layer Normalization
(LN), to inject robustness into model performance against varying image
artifacts. Through a systematic set of experiments, we show that GN and LN
provide better accuracy for various MR artifacts and distribution shifts.Comment: Accepted at MIDL 202
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